Linear Discriminant Analysis (LDA) has been successfully applied for dimension reduction in face recognition. However, LDA requires the transformation of a face image to a one-dimensional vector and this process can cause the correlation information among neighboring pixels to be disregarded. On the other hand, 2D-LDA uses 2D images directly without a transformation process and it has been shown to be superior to the traditional LDA. Nevertheless, there are some problems in 2D-LDA. First, it is difficult to determine the optimal number of feature vectors in a reduced dimensional space. Second, the size of rectangular windows used in 2D-LDA makes strong impacts on classification accuracies but there is no reliable way to determine an optimal window size. In this paper, we propose a new algorithm to overcome those problems in 2D-LDA. We adopt an ensemble approach which combines several classifiers obtained by utilizing various window sizes. And a practical method to determine the number of feature vectors is also presented. Experimental results demonstrate that the proposed method can overcome the difficulties with choosing an optimal window size and the number of feature vectors.

This paper presents only a matching algorithm based on Delaunay triangulation and Parametrization from the extracted minutiae points. This method maps local neighborhood of points of two different point sets to unit-circle using topology information by Delaunay triangulation method from feature points of real fingerprint. Then, a linked convex polygon that includes an interior point is constructed as one-ring which is mapped to unit-circle using Parametrization that keep shape preserve. In local matching, each area of polygon in unit-circle is compared. If the difference of two areas are within tolerance, two polygons are consider to be matched and then translation, rotation and scaling factors for global matching are calculated.

In this paper a method for detecting and extracting the face from the image in YCbCr spaceis proposed. The face region is obtained from the complex original image by using the difference method and the face color information is taken from the reduced face region throughthe Ellipse clustering method. The experimental results showed that the proposed method can efficiently detect and extract the face from the original image under the general light intensity except for low luminance.

Conventional filters using email header and body information equally judge whether an incoming email is spam or not. However this is unrealistic in everyday life because each person has different criteria to judge what is spam or not. To resolve this problem, we consider user preference information as well as email category information derived from the email content. In this paper, we have developed a personalized anti-spam system using ontologies constructed from rules derived in a data mining process. The reason why traditional content-based filters are not applicable to the proposed experimental situation is described. In also, several experiments constructing classifiers to decide email category and comparing classification rule learners are performed. Especially, an ID3 decision tree algorithm improved the overall accuracy around 17% compared to a conventional SVM text miner on the decision of email category. Some discussions about the axioms generated from the experimental dataset are given too.

This paper proposes a multipath routing protocol called cross-layer multipath AODV (CM-AODV) for wireless ad hoc networks, which selects multiple routes on demand based on the signal-to-interference plus noise ratio (SINR) measured at the physical layer. Note that AODV (Ad hoc On-demand Distance Vector) is one of the most popular routing protocols for mobile ad hoc networks. Each time a route request (RREQ) message is forwarded hop by hop, each forwarding node updates the route quality which is defined as the minimum SINR of serialized links in a route and is contained in the RREQ header. While achieving robust packet delivery, the proposed CM-AODV is amenable to immediate implementation using existing technology by neither defining additional packet types nor increasing packet length. Compared to the conventional multipath version of AODV (which is called AOMDV), CM-AODV assigns the construction of multiple paths to the destination node and makes it algorithmically simple, resulting in the improved performance of packet delivery and the less overhead incurred at intermediate nodes. Our performance study shows that CM-AODV significantly outperforms AOMDV in terms of packet delivery ratio and average end-to-end delay, and results in less routing overhead.

Recently, Hao et al. proposed a privacy preservation protocol based on group signature scheme for secure vehicular communications to overcome a well-recognized problems of secure VANETs based on PKI. However, although efficient group signature schemes have been proposed in cryptographic literatures, group signature itself is still a rather much time consuming operation. In this paper, we propose a more efficient privacy preservation protocol than that of Hao et al. In order to design a more efficient anonymous authentication protocol, we consider a key-insulated signature scheme as our cryptographic building block. We demonstrate experimental results to confirm that the proposed protocol is more efficient than the previous scheme.

In this paper, we present a secure biometric hashing scheme for face recognition by random fusion of global and local features. The Fourier-Mellin transform and Radon transform are adopted respectively to form specialized representation of global and local features, due to their invariance to geometric operations. The final biometric hash is securely generated by random weighting sum of both feature sets. A fourfold key is involved in our algorithm to ensure the security and privacy of biometric templates. The proposed biometric hash can be revocable and replaced by using a new key. Moreover, the attacker cannot obtain any information about the original biometric template without knowing the secret key. The experimental results confirm that our scheme has a satisfactory accuracy performance in terms of EER.

In this paper, we design a mobile phone based remote control system for PC using SKT WPAN platform and compare the presented platform with that of Windows Mobile. The usability of WPAN is one of the main issues which should be considered for the ubiquitous services. For easy development and easy use of the WPAN applications, SKT WPAN platform provides abstract WPAN APIs and WPAN Application Manager for ubiquitous services. In this paper, we implement a remote control application using the WPAN platform and show the validity of the platform comparing with other platforms. In the implemented application, we use WPAN abstract APIs on the mobile phone side and a general Bluetooth APIs on the PC for a connection between phone and PC. Through the implementation and comparison, we show that the WPAN application can be easily developed with WPAN platform.

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

, as an extended concept of the next generation IMT network, is a concept of basically supporting mobility using two steps of IP address (i.e. IPha (IP Host address) and IPra (IP routing address)) in IP backbone network. Current system has a shortcoming of excess usage of network resources caused by sending paging messages to all cells in LA (Location Area) in paging procedure. Considering the evolving direction of network, which is taking mobility with various speed and integration of devices into consideration, this shortcoming must be overcome. In this paper, we proposed a method to reduce time and memory for paging by maintaining current information of MN (Mobile Node) not in Active state with proxy server. Performance evaluation based on NS-2 simulations has shown that the efficiency of network resources is improved in the proposed method.

In wireless sensor networks (WSNs), cluster based data routing protocols have the advantages of reducing energy consumption and link maintenance cost. Unfortunately, most of clustering protocols have been designed for uniformly distributed sensor networks. However, some urgent situations do not allow thousands of sensor nodes being deployed uniformly. For example, air vehicles or balloons may take the responsibility for deploying sensor nodes hence leading a normally distributed topology. In order to improve energy efficiency in such sensor networks, in this paper, we propose a new cluster formation algorithm named DAEEC (Density Aware Energy-Efficient Clustering). In this algorithm, we define two kinds of clusters: Low Density (LD) clusters and High Density (HD) clusters. They are determined by the number of nodes participated in one cluster. During the data routing period, the HD clusters help the neighbor LD clusters to forward the sensed data to the central base station. Thus, DAEEC can distribute the energy dissipation evenly among all sensor nodes by considering the deployment density to improve network lifetime and average energy savings. Moreover, because the HD clusters are densely deployed they can work in a manner of our former algorithm EEVAR (Energy Efficient Variable Area Routing Protocol) to save energy. According to the performance analysis result, DAEEC outperforms the conventional data routing schemes in terms of energy consumption and network lifetime.

This paper proposes a multi-modal complex motion authoring tool for creating robot contents. The proposed tool is user-friendly and allows general users without much knowledge about robots, including children, women and the elderly, to easily edit and modify robot contents. Furthermore, the tool uses multi-modal data including graphic motion, voice and music to simulate user-created robot contents in the 3D virtual environment. This allows the user to not only view the authoring process in real time but also transmit the final authored contents to control the robot. The validity of the proposed tool was examined based on simulations using the authored multi-modal complex motion robot contents as well as experiments of actual robot motions.